Project Summary Despite advances in molecular and statistical genetics, identifying specific genes that contribute to the pathogenesis of common diseases has been challenging. This is due, at least in part, to the extensive genetic and phenotypic heterogeneity that characterize these diseases, the importance of non-genetic (e.g., environmental) factors that are rarely taken into account, and sample sizes that are often under powered to detect the likely modest effects of disease susceptibility genes. Here, we propose to integrate mapping and genome-wide expression profiling in order to find genes or regulatory regions that contribute to variability in susceptibility to and severity of cardiovascular diseases (CVD). To overcome the challenges described above, we propose to study the genetic basis for variation in physiological quantitative traits (QTs) that are associated with CVD susceptibility in a founder population with a remarkably uniform environment. By mapping genes for disease- associated physiological QTs, we will indirectly identify genes that influence susceptibility to or severity of the disease. Specifically, we plan to focus on four CVD-associated QTs, including a marker of general inflammation, for which associated genomic regions were previously identified in the Hutterites, a founder population of European descent. In order to hone in on the most promising candidate genes that underlie variation in these QTs, we will integrate several complementary approaches: (i) Use expression profiling in lymphoblastoid cell lines (LBL) from the Hutterites to identify candidate genes whose expression is associated with one or more of the QTs and that lie in genomic regions previously identified as linked to the QTs. (ii) Use a novel multi-species microarrays to compare expression profiles across primates and identify genes whose expression levels in the human liver, kidney, lymphocytes and heart evolved under natural selection and which lie in regions previously linked with the QTs, or whose expression is associated with variation in the QTs. (iii) Use an eQTL approach to map the genetic variants that influence the expression levels of the candidate genes identified in the first two approaches.